Table 3.

Example Projects and Key Learnings

Project description	Key problem identified	Quadruple aim(s) addressed	Current project status (as of June 2020)	Key learning(s)
Implementing a predictive model for clinical deterioration in the inpatient acute care setting	Unanticipated clinical deterioration resulting in unexpected escalations to the intensive care units and mortality	Better care	In implementation; conducting multiple PDSA cycles	Designing simulations built around real scenarios and early model prototypes accelerates learning the key features for successful workflow integration and model output delivery features. Involving the technical team from the beginning, along with the clinical team, allows for more rapid development and ideation.
Implementing a predictive mortality model to enable patient selection for end-of-life advance care planning discussions	Lower than desired incidence of end-of-life care planning conversations resulting in distress for patients and providers	Better care; workforce wellness	In implementation; conducting multiple PDSA cycles	Involving clinical, operational, and technical team members from the beginning leads to surprising and more feasible clinical integration workflows. Selecting a small focus group of front-line staff to engage in multiple PDSA cycles allows rapid multidisciplinary learning, and adjustments to model output delivery and workflows.
Designing an outpatient AI-enabled patient risk stratification and prediction tool	Ambulatory sensitive admissions and existing “hot spotting” tools that do not consider social determinants and clinician impressions found in unstructured medical data	Lower cost; better health	Assessing the utility of an AI solution; ideating on key features for success	Identifying a clinically relevant problem and conducting a current state analysis ensures that, before building the model, a clear AI task has been characterized and key drivers for operational success inform design early on. This also mitigates wasted time and resources.
Designing an AI-enabled at-home monitoring platform to close the data gap and enhance ability to engage patients between clinic visits	Low incidence of necessary behavior changes in patients with chronic diseases	Lower cost; better health	Analyzing the current system; determining key features for success	Involving the clinical, operational, and technical team members in an in-depth current state analysis leads to more innovative and surprising key features for clinical and operational success.
Designing an AI algorithm that can detect depression and anxiety based on audio and visual cues	Current depression screening methods require significant cognitive and clerical burden, limiting the ability to screen and identify patients with depression and anxiety	Better care, better health	Model in development; ideating on key features for success	Aligning with existing organizational priorities and quality improvement efforts helps secure leadership support and resourcing. Leveraging early-stage clinical integration workflows to determine the approach for curating training set data can be helpful.
Assessing the feasibility and acceptability of an AI-powered tool to assist primary care providers with diagnosing skin conditions	Suboptimal referrals, delays in care, and errors in diagnoses due to a shortage of dermatologists, and the burden of diagnosis placed on primary care providers	Lower cost; better care	Ideating on key features for success and clinical integration workflow for the pilot study	Including technical, clinical, and operational stakeholders, and leveraging frameworks such as PDSA and UTAUT²¹ enables the team to learn together iteratively how an AI model works versus does an AI model work.
Designing, and assessing the acceptability of an AI-enabled model for pre-visit planning and intra-visit care management	Scarcity of integrated tools to help patients efficiently and effectively organize and manage their personal care plans between visits	Better care	Analyzing the current system; ideating on key features for success	Conducting extensive user experience and current system analyses from the patient and provider perspective ensures that technology features and data infrastructure are designed with workflow integration in mind from the beginning.
Assessing the acceptability of AI-enabled documentation of various aspects of the clinic visit note	Physician burnout due to burden of documentation	Workforce wellness	Project completed	Conducting extensive user experience and current state analyses of existing human-driven documentation practices provides important insights into key features for success and factors that influence trust and acceptability of proposed AI technologies.